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A checklist for auditing AI systems

Governance and Compliance

Confirm the establishment of an AI governance framework.

Ensure compliance with relevant regulations and standards (e.g., GDPR, CCPA).

Review documentation of AI policies and procedures.

Verify the roles and responsibilities of the AI oversight committee.

Assess transparency measures for AI decision-making processes.

Data Management

Evaluate data sources for quality and bias.

Check for appropriate data collection and consent mechanisms.

Ensure data privacy and security protocols are in place.

Review data labeling processes for accuracy and consistency.

Confirm data retention and deletion policies are followed.

Model Development and Validation

Assess the methodology used for model selection and training.

Verify the presence of comprehensive testing protocols.

Review performance metrics and evaluation results.

Ensure robustness against adversarial attacks and vulnerabilities.

Confirm documentation of model revisions and updates.

Ethical Considerations

Evaluate the ethical implications of the AI system's decisions.

Assess measures for mitigating bias and discrimination.

Review stakeholder engagement processes for ethical feedback.

Ensure alignment with organizational values and ethical standards.

Confirm accountability mechanisms for AI outcomes.

Deployment and Monitoring

Verify deployment procedures and change management protocols.

Assess real-time monitoring and logging practices.

Review processes for user feedback and incident reporting.

Confirm ongoing performance evaluations and model retraining plans.

Ensure mechanisms for addressing unintended consequences are in place.

Stakeholder Communication

Review communication strategies regarding AI capabilities and limitations.

Identify current communication methods.
Evaluate clarity and comprehension of AI functionalities.
Assess the accuracy of information shared.
Gather feedback from stakeholders on communication effectiveness.

Assess transparency in sharing AI system decisions with users.

Review documentation of decision-making processes.
Ensure explanations of AI decisions are accessible.
Evaluate user understanding of AI outcomes.
Gather user feedback on perceived transparency.

Confirm channels for addressing stakeholder concerns and feedback.

Identify existing feedback mechanisms.
Evaluate responsiveness of communication channels.
Ensure stakeholders know how to submit concerns.
Assess satisfaction with existing feedback processes.

Ensure regular updates are provided to stakeholders about AI governance.

Establish a schedule for updates.
Identify key topics for communication.
Ensure updates are distributed through appropriate channels.
Gather feedback on the effectiveness of updates.

Verify the availability of educational resources on AI ethics and usage.

Identify current educational materials provided.
Assess the relevance and accessibility of resources.
Gather stakeholder feedback on resource effectiveness.
Ensure resources are regularly updated and maintained.

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